Health IT and Clinical Informatics

The Role of Health Information Technology in Transforming the Field of Obstetrics and Gynecology

What Is Health Information Technology?

Health information technology (HIT) encompasses the electronic systems health care professionals use to facilitate the processing, storage, retrieval, and sharing of health information. For a majority of ob-gyns, HIT is in the form of electronic health records (EHRs).

HIT also includes …

Advanced imaging tools
Decision support systems
Telemedicine
Artificial Intelligence (AI)
Other electronic devices that enhance diagnostic accuracy and facilitate remote patient monitoring

Role of HIT in Obstetric and Gynecologic Health Care

EHRs have become the standard of clinical practice due to the implementation mandate by insurance payers, recognition of their potential benefits, and government incentivization for their usage. Embedded within most EHRs is the patient portal that enables patients to access their medical records, view and respond to messages from their physician, and see their laboratory results in real time. This allows for direct patient–physician engagement, leading to improved patient care.

Electronic or digital devices such as mobile apps and wearable devices have revolutionized how patients track and monitor their health, fetal growth, and overall well-being. With the use of telehealth services such as virtual consultation, remote monitoring, and education, patients experience increased access to care and enhanced physician–patient interaction, which lead to improved health outcomes for patients receiving obstetric and gynecologic care.

Gynecologists use telehealth for consultation on diverse conditions, such as menstrual disorders, infertility, menopause, and cancers of the reproductive organs. ACOG recognizes the importance of telehealth to increase access to critical health care services and therefore encourages health care professionals to continue to offer preventive services to their patients through telehealth platforms whenever appropriate. ACOG engages in a variety of advocacy efforts related to telehealth services such as regulatory comment letters, legislative action, and payer-focused advocacy that ensures coverage and payment of telehealth services.

In addition, ACOG engages with the Office of the National Coordinator for Health IT (ONC) United States Core Data for Interoperability on the access, exchange, and use of electronic health information to facilitate interoperability of maternity health records across health care settings. More importantly, ACOG recognizes the ONC’s efforts to emphasize that personal health records for pregnant patients help improve their health and safety as they transfer from one level of care to another during pregnancy. In support of this effort, the ONC United States Core Data for Interoperability proposed adding new data classes and elements to the maternal health dataset, such as those related to anxiety and depression screening and breastfeeding intention, including the previously considered pregnancy intention screening.

HHS’s assistant secretary for technology policy reached out to ACOG and AMA requesting support for updating the electronic data dictionary and required fields for obstetric EHRs. ACOG supports and recommends the addition of 14 new data elements to the current data elements, which will promote high quality care, equitable outcomes, and maternal health research.

AI in Obstetrics and Gynecology

AI is defined by Merriam-Webster as “software designed to imitate aspects of intelligent human behavior”. The AMA prefers to use the term “augmented intelligence” to reflect its perspective that AI tools and services support rather than explicitly replace human decision-making. AI has been around for some time now but has made significant progress in recent years in transforming the practice of medicine and the delivery of health care. AI has the potential to anticipate problems or deal with issues as they come up, thus operating in an intended, logical, and adaptive approach. It can be a powerful tool in health care settings, capable of providing faster, more accurate diagnoses and lesser medical errors. While level of adoption varies across specialties, there is significant alignment in areas of opportunities for AI in obstetrics and gynecology, including, but not limited to, imaging analysis, expanded patient screening, reducing administrative burden, and supporting clinical decision making. The view that AI amplifies and augments rather than replaces human intelligence emphasizes the need to ensure that when employing AI in health care, it does not replace the important elements of the human interaction in medicine but rather focuses on improving the efficiency and effectiveness of that interaction.

Some Use Cases of AI in Obstetrics and Gynecology

AI has a wide range of potential use cases in health care, especially in the field of obstetrics and gynecology, ranging from supporting health care practitioners in clinical decision making, personalized medicine, improving maternal and fetal outcomes, and optimizing gynecological interventions. AI has the transformative potential to enhance diagnostic accuracy and interpretation and reduce administrative burdens, ultimately improving patient health outcomes.

Ultrasound Imaging

AI has shown remarkable possibilities in improving diagnostic imaging, mainly in ultrasound, a noninvasive technique for diagnosing pregnancy, and MRI. Additionally, machine learning has been found useful in identifying fetal structures and organs, which help diagnose congenital abnormalities when used by skilled ob-gyn personnel.

MRI

In obstetrics, MRI is used to distinguish between various fetal brain conditions and assess the severity of placenta previa. In gynecology, AI models have demonstrated effectiveness in interpreting MRI scans for conditions such as endometriosis, fibroids, and ovarian tumors. AI-driven image analysis helps to differentiate between benign and malignant masses, thus promoting early and accurate diagnosis.

Fetal Heart Monitoring

Health care professionals have also found AI to be effective in monitoring fetal heart rate and diagnosing related high-risk complications. It is currently used to monitor fetal heart rate during labor and uterine contractions by analyzing cardiotocographs and estimating possible outcomes. This tool identifies patterns and deviations in fetal heart rate, movements, and uterine contractions, providing real-time insights into fetal health.

Maternal Health Monitoring

Maternal health is another potential area where AI is effective for evaluating vital signs, such as blood pressure and heart rate; laboratory tests, such as those for blood glucose levels and proteinuria; and biomarkers, such as placental growth factors and cytokines. AI uses real-time monitoring and analyzes trends to detect early signs of maternal complications such as preeclampsia, gestational diabetes, or infection, enabling timely interventions.

Preterm Birth Prediction

The AI model has proven effective in predicting pregnancy-related complications, such as preterm birth, by allowing early prediction, personalized risk, and identification of high-risk pregnancies. AI algorithms that analyze biological markers, such as cytokines and cervical length, and imaging techniques such as ultrasound and MRI improve the detection of early signs of preterm labor or complications like cervical insufficiency. This in turn provides health care practitioners with actionable insights for preemptive management.

Gynecology Oncology

Pathologists can utilize AI Algorithms to identify cancerous cells with greater accuracy and efficiency, reducing diagnostic errors and providing personalized treatment plans tailored to each patients needs.

AI has also demonstrated significant potential in gynecologic oncology for cancer treatment by analyzing medical imaging data, such as ultrasound, MRI, and CT scans, to detect early signs of cancer. It can help tailor chemotherapy and radiation therapy based on a tumor’s genetic profile, thereby improving patient outcomes. AI can detect difficult abnormalities that may indicate the presence of tumors or precancerous cells, allowing early intervention and increasing survival rates. Pathologists can use AI algorithms to identify cancerous cells with greater accuracy and efficiency, reducing diagnostic errors and providing personalized treatment plans tailored to each patient’s health needs.

ACOG’s Committee on Health Economics and Coding monitors the impact of AI on practice management, administrative burden, and reimbursement. Members work closely with the AMA AI collaborative workgroup to discuss the current use and opportunities of AI tools and how these tools can be regulated when incorporated into clinical practice guidelines.